Science Curiosity Master Doc
Libraries
library(rio)
library(here)
library(janitor)
library(reshape2)
library(tidyverse)
library(ggplot2)
library(lme4)
library(knitr)
library(Hmisc)
library(viridis)
library(kableExtra)
library(ggeffects)Data
df1s <- import(here("analysis-data", "cleaned-data1031.csv"))
df1s %>%
nrow()## [1] 538df2s <- df1s %>%
filter(aot_zscored > -3)
df2s %>%
nrow()## [1] 534df3s <- df2s %>%
filter(vl_zscored > -3)
df4s <- df1s %>%
filter(aot_zscored > -3 & vl_zscored > -3 & nfc_zscored > -3)
df4s %>%
nrow()## [1] 533Methods
Abstract. In a preregistered study, we test the relationship between science curiosity and motivated reasoning, extending work by Kahan et al. (2017) in which high science curiosity was associated with less selective exposure to politicized science news stories. N=541 participants completed a motivated reasoning task in which they viewed graphs of NASA climate change data and predicted future trends in those data before completing a validated science curiosity inventory We predicted that participants higher in science curiosity would engage in less motivated reasoning (measured through selective endpoint bias) than those lower in science curiosity, controlling for cognitive ability and political ideology.
Data collection
This study was run on Amazon Mechanical Turk via CloudResearch and utilized CAIDe lab’s Cohort 4. It was conducted in two parts: cognitive ability and demographics (including political ideology) were collected in a baseline survey, and participants completed the motivated reasoning items and science curiosity scale in a second survey. Our target sample size was N=750, but we didn’t received enough responses.
Baseline: August 16-17, 2021
- SNS
- NFC
- Climate risk perception
- NSE
- Adaptive ONS (and confidence)
- RPM (and confidence)
- Vocab (and confidence)
- Verbal logic (analogies; and confidence)
- Political knowledge (and confidence)
- AOT
- COVID-19 vaccine screener
- Demographics (ANES political ideology)
Current Study: July 13-26, 2022
- Motivated reasoning task (slope direction)
- Practice item/attention check (0)
- 4 ambiguous political items
- 2 conservative: arctic sea ice (-) and global temperature index (+)
- 2 liberal: ozone hole (-) and US air quality (+)
- All participants saw one of these four items, randomly-selected, as their first item after the practice item
- 2 unambiguous political items
- Both conservative: ice sheets (-) and CO2 (+)
- 1 ambiguous nonpolitical item
- Bacteria population density (-)
- 1 unambiguous nonpolitical item
- Earthquake magnitude (0)
- Modified version of Kahan et al. (2017) science curiosity scale
- Data quality
Preregistration
A preregistration was posted on OSF. Below is a summary of key points (using original language found in the preregistration).
Variables
- Item congruence
- Item-level endpoint bias
- Ideologically-motivated response inconsistency (IMRI)
- Science curiosity
- Cognitive ability
- Political ideology
- Climate change risk perception
- Demographics
Hypotheses
- H1. Trait science curiosity will moderate the relationship between item congruence and item-level endpoint bias. Higher (vs. lower) trait science curiosity will be associated with less endpoint bias on congruent (vs. incongruent) items.
- H2. There will be a main effect of science curiosity on ideologically-motivated response inconsistency. Participants higher (vs. lower) in science curiosity will be less likely to demonstrate ideologically-motivated response inconsistencies between congruent and incongruent items.
Analysis plan
- H1. We will use a multilevel modeling approach with random intercepts and slopes, wherein item-level performance values are nested within participants. The outcome variable in the model will be performance scores on all items for all participants (resulting in 3N observations). The predictor variables will be item congruence and science curiosity scores. Cognitive ability and political ideology will be included as covariates.
- Manipulation check. With this model, we will also test whether there is a significant relationship between item congruence and item-level endpoint bias, such that participants exhibit greater endpoint bias on congruent items (vs. incongruent items).
- H2. We will test a logistical regression model wherein IMRI values (0 or 1) are predicted by science curiosity scores, controlling for cognitive ability and political ideology.
- Data exclusion. We will exclude participant data for any of the following reasons:
- a participant’s IP address is outside of the US,
- a participant responds to a self-report item suggesting exclusion (e.g., reports random responding or significant distraction during survey completion),
- a participant exhibits poor performance (e.g., z < -3) on control/filler graph items,
- a participant fails an attention check item,
- a participant submits a nonsense response on an open-ended item,
- and/or a participant exhibits a survey completion time more extreme than +/-3 SD of the mean.
- Exploratory analyses.
- We plan to explore the predictive power of participants’ climate change risk perceptions (a more proximal measure) relative to their reported political ideology. Similarly, we may also explore numeracy as a covariate instead of or alongside their performance on the RPM task.
- The coding scheme for the IMRI variable has not yet been tested with this paradigm, so H2 and its analyses are ultimately exploratory. With this preliminary study, we may develop a better approach for testing H2, which we could then implement in future studies.
- We may utilize participants’ 10-year predictions in an exploratory analysis. Specifically, we may test the utility of examining the difference scores or slopes created by participants’ 10- and 20-year predictions for each item in measuring endpoint bias.
Materials
Coding motivated reasoning items
Conservative-congruent item prompt.
Response options.
The politicized filler item related to CO2 levels.
Data Prepping and Cleaning
Final df is excluding:
- Duplicates
- Unfinished
- Reported cheating
- Reported bad response
- Unapproved
- Did not consent
- Was not in both dfs (b and s1)
- Outliers:
- Duration (1)
- Science Curiosity (3)
- Raven’s Progressive Matrices (2)
Attention check. 381 people failed the attention check, which is honestly pretty impressive! Was clearly not a good attention check. I don’t think this is a red flag for data quality, though.
Descriptives
N\(_{total}\) = 533
- Political ideology: 41.09% conservative, 49.53% liberal, 9.38% moderate
- N\(_{con}\) = 219, N\(_{lib}\) = 264, N\(_{mod}\) = 50
- Age: Mean\(_{age}\) = 43.02 (range: 19-78)
- Gender: 46.9% women, 51.97% men, 0.38% non-binary folks, 0.75% preferred not to answer
- Race: 83.86% White, 5.25% Black, 2.63% Hispanic, 6.38% Asian
Descriptives for conservatism measure:
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 533 | 3.65 | 1.79 | 4 | 3.62 | 2.97 | 1 | 7 | 6 | 0.22 | -1.14 | 0.08 |
| Mean | SE | Skew | Min | Max | Mean | SE | Skew | Min | Max | Mean | Min | Max | SE | Skew | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Conservatism | 5.55 | 0.05 | 0.34 | 4.5 | 7 | 4.00 | 0.00 | NaN | 4 | 4 | 2.08 | 1 | 3 | 0.04 | -0.11 |
| SciCuriosity | 24.03 | 0.37 | -0.09 | 9.0 | 37 | 24.68 | 1.01 | -0.16 | 10 | 40 | 25.18 | 9 | 40 | 0.35 | -0.20 |
| RPM | 5.53 | 0.10 | -0.19 | 1.0 | 10 | 5.50 | 0.19 | 0.25 | 3 | 9 | 5.72 | 1 | 10 | 0.10 | -0.08 |
| ConArctic | -1.11 | 0.09 | 0.79 | -3.0 | 3 | -1.24 | 0.18 | 0.84 | -3 | 2 | -1.73 | -3 | 3 | 0.07 | 1.57 |
| ConTemp | -1.39 | 0.08 | 1.00 | -3.0 | 2 | -1.14 | 0.18 | 1.41 | -3 | 3 | -1.97 | -3 | 2 | 0.06 | 1.33 |
| ConFillCO2 | -1.97 | 0.07 | 1.40 | -3.0 | 2 | -1.68 | 0.17 | 1.32 | -3 | 2 | -2.27 | -3 | 1 | 0.06 | 1.65 |
| ConFillIce | -1.85 | 0.09 | 1.44 | -3.0 | 3 | -1.86 | 0.18 | 1.06 | -3 | 1 | -2.33 | -3 | 3 | 0.06 | 2.50 |
| LibOzone | -0.31 | 0.10 | 0.44 | -3.0 | 3 | -0.52 | 0.19 | 0.15 | -3 | 3 | -0.22 | -3 | 3 | 0.09 | 0.48 |
| LibAir | -0.82 | 0.11 | 0.71 | -3.0 | 3 | -1.16 | 0.20 | 0.78 | -3 | 3 | -0.80 | -3 | 3 | 0.10 | 0.82 |
| FillBacteria | -0.18 | 0.08 | 0.24 | -3.0 | 3 | -0.58 | 0.17 | 0.55 | -3 | 3 | -0.35 | -3 | 3 | 0.08 | 0.22 |
| FillQuake | 0.29 | 0.03 | 0.91 | 0.0 | 1 | 0.24 | 0.06 | 1.18 | 0 | 1 | 0.33 | 0 | 1 | 0.03 | 0.74 |
int_means <- df4s %>%
group_by(sc3, rpm3, ideo2) %>%
summarise_at(vars("ConArctic", "ConTemp", "LibOzone", "LibAir"), funs(mean, se_f, length))
int_means_tab <- int_means %>%
select(-contains("length")) %>%
select(sc3, rpm3, ideo2, contains("Arctic"), contains("Temp"), contains("Ozone"), contains("Air"))“Quick and dirty” 3-way interaction means
| SciCur | Con1_M | Con1_SE | Con2_M | Con2_SE | Lib1_M | Lib1_SE | Lib2_M | Lib2_SE | |
|---|---|---|---|---|---|---|---|---|---|
| Con | Above Avg | -1.25 | 0.37 | -1.88 | 0.23 | -0.38 | 0.46 | -1.12 | 0.55 |
| Con | Avg | -1.16 | 0.10 | -1.39 | 0.09 | -0.33 | 0.10 | -0.86 | 0.11 |
| Con | Below Avg | -0.25 | 0.37 | -1.08 | 0.26 | 0.00 | 0.37 | 0.00 | 0.35 |
| Lib | Above Avg | -1.26 | 0.38 | -2.05 | 0.14 | 0.05 | 0.36 | -0.68 | 0.50 |
| Lib | Avg | -1.77 | 0.08 | -1.98 | 0.06 | -0.23 | 0.09 | -0.84 | 0.10 |
| Lib | Below Avg | -1.64 | 0.27 | -1.71 | 0.34 | -0.43 | 0.39 | -0.29 | 0.42 |
| Mod | Above Avg | -1.50 | 0.22 | -1.33 | 0.21 | -0.67 | 0.49 | -1.17 | 0.40 |
| Mod | Avg | -1.26 | 0.22 | -1.13 | 0.21 | -0.56 | 0.22 | -1.10 | 0.24 |
| Mod | Below Avg | -0.80 | 0.58 | -1.00 | 0.77 | 0.00 | 0.45 | -1.60 | 0.68 |
| Note: | |||||||||
|
* Higher scores indicate MORE endpoint bias * M = mean, SE = standard error * Item names: Con1 = arctic sea ice, Con2 = global temperature index, Lib1 = ozone layer hole, Lib2 = US air quality |
Table color coding. The areas of the table highlighted in gray indicate for each item whether the endpoint bias was congruent with conservative or liberal beliefs. For example, Con1 and Con2 portrayed endpoint bias congruent with conservative beliefs, so the top gray square highlights the Ms and SEs of conservative participants for each item, grouped by science curiosity scores.
Science curiosity groupings. Above avg: z > 1.5, Avg: -1.5 <= z >= 1.5, Below Avg: z < -1.5
What would we predict? If our hypothesis were correct, in the gray areas of this table, there would be a negative relation between science curiosity and endpoint bias scores, indicating that higher science curiosity scores were associated with less biased responding.
Looking at these means, what do we see? The second conservative item (Con2; related to global temperature) does demonstrate directional consistency with this hypothesis among conservatives (and liberals, but with larger SEs). Furthermore, the second liberal item (Lib2; related to air quality) displays directional consistency among conservatives but not liberals.
Is it endpoint bias?
Unlike other studies, we included a few filler items in the motivated reasoning task, two of which were politicized but with clear trends and no endpoint discrepancy.
| SciCur | Con1_M | Con1_SE | Con2_M | Con2_SE | Fill1_M | Fill1_SE | Fill2_M | Fill2_SE | |
|---|---|---|---|---|---|---|---|---|---|
| Con | Above Avg | -1.25 | 0.37 | -1.88 | 0.23 | -2.25 | 0.25 | -2.62 | 0.18 |
| Con | Avg | -1.16 | 0.10 | -1.39 | 0.09 | -1.97 | 0.07 | -1.84 | 0.09 |
| Con | Below Avg | -0.25 | 0.37 | -1.08 | 0.26 | -1.75 | 0.18 | -1.50 | 0.36 |
| Lib | Above Avg | -1.26 | 0.38 | -2.05 | 0.14 | -2.16 | 0.21 | -1.95 | 0.36 |
| Lib | Avg | -1.77 | 0.08 | -1.98 | 0.06 | -2.30 | 0.06 | -2.37 | 0.06 |
| Lib | Below Avg | -1.64 | 0.27 | -1.71 | 0.34 | -1.93 | 0.32 | -2.14 | 0.18 |
| Mod | Above Avg | -1.50 | 0.22 | -1.33 | 0.21 | -1.67 | 0.33 | -1.50 | 0.56 |
| Mod | Avg | -1.26 | 0.22 | -1.13 | 0.21 | -1.64 | 0.21 | -1.85 | 0.21 |
| Mod | Below Avg | -0.80 | 0.58 | -1.00 | 0.77 | -2.00 | 0.00 | -2.40 | 0.24 |
| Note: | |||||||||
|
* Higher scores indicate MORE endpoint (?) bias * M = mean, SE = standard error * Item names: Con1 = arctic sea ice, Con2 = global temperature index, Fill1 = CO2 levels, Lib2 = ice sheet area |
What’s going on? As we would expect, because their trends were not ambiguous, endpoint bias scores were lower for the filler items compared to the motivated reasoning items. Interestingly, though, they were still significantly polarized by ideology, despite the fact that we instructed participants to make their prediction “based on the given information.”
So what? Based on these results, we can’t necessarily determine whether endpoint bias was the mechanism behind polarized responding. In the case of these filler items, we know that it was not simply based on the definition of endpoint bias. Does this indicate, even in our motivated reasoning items, that this is not so much motivated reasoning as biased responding based on existing attitudes? Or are two different mechanisms at play between the filler and the endpoint items? We’d need to know more about the precise reasoning (or lack thereof) that formed their responses.
By item
Conservative #1: Arctic
df4s %>%
ggplot(aes(x = arctic20)) +
geom_histogram(binwidth = 1)
arcmod1 <- lm(ConArctic ~ 1, data = df4s)
summary(arcmod1)##
## Call:
## lm(formula = ConArctic ~ 1, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5704 -0.5704 -0.5704 0.4296 4.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.42964 0.05636 -25.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.301 on 532 degrees of freedomarcmod2 <- lm(ConArctic ~ rav_scoredz, data = df4s)
summary(arcmod2)##
## Call:
## lm(formula = ConArctic ~ rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8142 -0.6559 -0.4976 0.4496 4.3969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.42885 0.05630 -25.380 <2e-16 ***
## rav_scoredz -0.08432 0.05752 -1.466 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 531 degrees of freedom
## Multiple R-squared: 0.004031, Adjusted R-squared: 0.002155
## F-statistic: 2.149 on 1 and 531 DF, p-value: 0.1433anova(arcmod1, arcmod2)## Analysis of Variance Table
##
## Model 1: ConArctic ~ 1
## Model 2: ConArctic ~ rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 900.61
## 2 531 896.98 1 3.6302 2.149 0.1433arcmod3 <- lm(ConArctic ~ sc_scoredz + rav_scoredz, data = df4s)
summary(arcmod3)##
## Call:
## lm(formula = ConArctic ~ sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8931 -0.6466 -0.4877 0.4684 4.3371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.42923 0.05629 -25.391 <2e-16 ***
## sc_scoredz -0.06523 0.05877 -1.110 0.268
## rav_scoredz -0.07882 0.05772 -1.365 0.173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.299 on 530 degrees of freedom
## Multiple R-squared: 0.006341, Adjusted R-squared: 0.002591
## F-statistic: 1.691 on 2 and 530 DF, p-value: 0.1853anova(arcmod1, arcmod3)## Analysis of Variance Table
##
## Model 1: ConArctic ~ 1
## Model 2: ConArctic ~ sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 900.61
## 2 530 894.90 2 5.7106 1.691 0.1853arcmod4 <- lm(ConArctic ~ ArcticCong + sc_scoredz + rav_scoredz, data = df4s)
summary(arcmod4)##
## Call:
## lm(formula = ConArctic ~ ArcticCong + sc_scoredz + rav_scoredz,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8698 -0.6499 -0.4741 0.4710 4.3609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4485336 0.0591555 -24.487 <2e-16 ***
## ArcticCong 0.0002049 0.0001934 1.060 0.290
## sc_scoredz -0.0654266 0.0587626 -1.113 0.266
## rav_scoredz -0.0772759 0.0577318 -1.339 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.299 on 529 degrees of freedom
## Multiple R-squared: 0.008446, Adjusted R-squared: 0.002823
## F-statistic: 1.502 on 3 and 529 DF, p-value: 0.2131anova(arcmod1, arcmod4)## Analysis of Variance Table
##
## Model 1: ConArctic ~ 1
## Model 2: ConArctic ~ ArcticCong + sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 900.61
## 2 529 893.00 3 7.6068 1.502 0.2131df4s_lc <- df4s %>%
filter(conservatism != 4) %>%
select(temp20_ch, arctic20_ch, ozone20_ch, airqual20_ch, co220_ch,
icesheets20_ch, conservatism, num_score_b, sc_scored) %>%
drop_na() %>%
mutate(con_z = as.numeric(scale(conservatism)),
sc_zed = as.numeric(scale(sc_scored)),
ons_z = as.numeric(scale(num_score_b)))
testmod1 <- lm(arctic20_ch ~ con_z * sc_zed, data = df4s_lc)
summary(testmod1)##
## Call:
## lm(formula = arctic20_ch ~ con_z * sc_zed, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3600 -0.9041 -0.2460 0.5739 4.6442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.534292 0.061229 41.390 < 2e-16 ***
## con_z 0.360285 0.061279 5.879 8.15e-09 ***
## sc_zed -0.003831 0.061268 -0.063 0.950
## con_z:sc_zed -0.068253 0.063439 -1.076 0.283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.279 on 440 degrees of freedom
## Multiple R-squared: 0.07687, Adjusted R-squared: 0.07058
## F-statistic: 12.21 on 3 and 440 DF, p-value: 1.088e-07testmod2 <- lm(arctic20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(testmod2)##
## Call:
## lm(formula = arctic20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3523 -0.8532 -0.2321 0.5544 4.6304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.539902 0.061719 41.153 < 2e-16 ***
## sc_zed -0.008175 0.063402 -0.129 0.8975
## con_z 0.364432 0.061757 5.901 7.26e-09 ***
## ons_z 0.002208 0.062912 0.035 0.9720
## sc_zed:con_z -0.084200 0.065493 -1.286 0.1993
## sc_zed:ons_z -0.009953 0.064506 -0.154 0.8775
## con_z:ons_z 0.111197 0.063904 1.740 0.0826 .
## sc_zed:con_z:ons_z -0.053272 0.066113 -0.806 0.4208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.279 on 436 degrees of freedom
## Multiple R-squared: 0.08554, Adjusted R-squared: 0.07086
## F-statistic: 5.826 on 7 and 436 DF, p-value: 1.783e-06testmod3 <- lm(arctic20_ch ~ sc_zed * ons_z + con_z, data = df4s_lc)
summary(testmod3)##
## Call:
## lm(formula = arctic20_ch ~ sc_zed * ons_z + con_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1820 -0.9898 -0.2494 0.5645 4.7713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.543922 0.061115 41.625 < 2e-16 ***
## sc_zed -0.005189 0.062239 -0.083 0.934
## ons_z 0.002390 0.061387 0.039 0.969
## con_z 0.360910 0.061782 5.842 1.01e-08 ***
## sc_zed:ons_z -0.012706 0.062284 -0.204 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.282 on 439 degrees of freedom
## Multiple R-squared: 0.07453, Adjusted R-squared: 0.0661
## F-statistic: 8.839 on 4 and 439 DF, p-value: 7.175e-07# rcorr(df4s$num_score_b, df4s$sc_scoredz)
sjPlot::plot_model(testmod2, type = "int", mdrt.values = "meansd")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
# terms = c("sc_zed", "ons_z", "con_z")
mean(df4s_lc$conservatism)## [1] 3.574324sjPlot::plot_model(testmod3, type = "int", mdrt.values = "meansd")
testmod4 <- lm(arctic20_ch ~ sc_zed * ons_z, data = df4s_lc)
summary(testmod4)##
## Call:
## lm(formula = arctic20_ch ~ sc_zed * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7053 -0.5958 -0.5105 0.4727 4.6225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.54643 0.06337 40.182 <2e-16 ***
## sc_zed -0.05152 0.06401 -0.805 0.421
## ons_z -0.02608 0.06346 -0.411 0.681
## sc_zed:ons_z -0.04087 0.06439 -0.635 0.526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 440 degrees of freedom
## Multiple R-squared: 0.002593, Adjusted R-squared: -0.004207
## F-statistic: 0.3813 on 3 and 440 DF, p-value: 0.7665sjPlot::plot_model(testmod4, type = "int", mdrt.values = "meansd")
testmod5 <- lm(temp20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(testmod5)
sjPlot::plot_model(testmod5, type = "int", mdrt.values = "meansd")
df5s_lc <- df4s %>%
filter(conservatism != 4) %>%
select(co220_ch, conservatism, num_score_b, sc_scored) %>%
drop_na() %>%
mutate(con_z = as.numeric(scale(conservatism)),
sc_zed = as.numeric(scale(sc_scored)),
ons_z = as.numeric(scale(num_score_b)))
testmod6 <- lm(co220_ch ~ sc_zed * con_z * ons_z, data = df5s_lc)
summary(testmod6)
sjPlot::plot_model(testmod6, type = "int", mdrt.values = "meansd")
df6s_lc <- df4s %>%
filter(conservatism != 4) %>%
select(icesheets20_ch, conservatism, num_score_b, sc_scored) %>%
drop_na() %>%
mutate(con_z = as.numeric(scale(conservatism)),
sc_zed = as.numeric(scale(sc_scored)),
ons_z = as.numeric(scale(num_score_b)))testmod6 <- lm(temp20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(testmod6)##
## Call:
## lm(formula = temp20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4757 -0.7484 -0.0852 0.3918 3.6786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.301183 0.051709 44.503 < 2e-16 ***
## sc_zed -0.123117 0.053118 -2.318 0.0209 *
## con_z 0.363198 0.051741 7.020 8.57e-12 ***
## ons_z -0.000589 0.052708 -0.011 0.9911
## sc_zed:con_z -0.032340 0.054870 -0.589 0.5559
## sc_zed:ons_z -0.012788 0.054044 -0.237 0.8131
## con_z:ons_z 0.124920 0.053539 2.333 0.0201 *
## sc_zed:con_z:ons_z -0.113404 0.055390 -2.047 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.072 on 436 degrees of freedom
## Multiple R-squared: 0.1353, Adjusted R-squared: 0.1214
## F-statistic: 9.748 on 7 and 436 DF, p-value: 2.678e-11sjPlot::plot_model(testmod6, type = "int", mdrt.values = "meansd")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
# arctic one (below)
testmod8 <- lm(arctic20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(testmod8)##
## Call:
## lm(formula = arctic20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3523 -0.8532 -0.2321 0.5544 4.6304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.539902 0.061719 41.153 < 2e-16 ***
## sc_zed -0.008175 0.063402 -0.129 0.8975
## con_z 0.364432 0.061757 5.901 7.26e-09 ***
## ons_z 0.002208 0.062912 0.035 0.9720
## sc_zed:con_z -0.084200 0.065493 -1.286 0.1993
## sc_zed:ons_z -0.009953 0.064506 -0.154 0.8775
## con_z:ons_z 0.111197 0.063904 1.740 0.0826 .
## sc_zed:con_z:ons_z -0.053272 0.066113 -0.806 0.4208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.279 on 436 degrees of freedom
## Multiple R-squared: 0.08554, Adjusted R-squared: 0.07086
## F-statistic: 5.826 on 7 and 436 DF, p-value: 1.783e-06sjPlot::plot_model(testmod8, type = "int", mdrt.values = "meansd")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
testmod9 <- lm(co220_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(testmod9)##
## Call:
## lm(formula = co220_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3258 -0.7251 -0.0507 0.3201 3.9825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.85823 0.04646 39.999 < 2e-16 ***
## sc_zed -0.08727 0.04772 -1.829 0.06812 .
## con_z 0.16863 0.04649 3.628 0.00032 ***
## ons_z 0.05009 0.04735 1.058 0.29079
## sc_zed:con_z -0.01629 0.04930 -0.331 0.74116
## sc_zed:ons_z 0.01898 0.04855 0.391 0.69612
## con_z:ons_z 0.07046 0.04810 1.465 0.14372
## sc_zed:con_z:ons_z -0.04106 0.04976 -0.825 0.40982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9629 on 436 degrees of freedom
## Multiple R-squared: 0.04678, Adjusted R-squared: 0.03148
## F-statistic: 3.057 on 7 and 436 DF, p-value: 0.003757sjPlot::plot_model(testmod9, type = "int", mdrt.values = "meansd")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
testmod10 <- lm(temp20_ch ~ ons_z * con_z, data = df4s_lc)
summary(testmod10)##
## Call:
## lm(formula = temp20_ch ~ ons_z * con_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0929 -0.7396 -0.0982 0.4378 3.7926
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.31177 0.05131 45.054 < 2e-16 ***
## ons_z 0.01272 0.05174 0.246 0.80598
## con_z 0.37340 0.05138 7.268 1.68e-12 ***
## ons_z:con_z 0.13990 0.05257 2.661 0.00807 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.077 on 440 degrees of freedom
## Multiple R-squared: 0.119, Adjusted R-squared: 0.113
## F-statistic: 19.82 on 3 and 440 DF, p-value: 4.587e-12sjPlot::plot_model(testmod10, type = "int", mdrt.values = "meansd")
postermod <- lm(temp20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
summary(postermod)##
## Call:
## lm(formula = temp20_ch ~ sc_zed * con_z * ons_z, data = df4s_lc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4757 -0.7484 -0.0852 0.3918 3.6786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.301183 0.051709 44.503 < 2e-16 ***
## sc_zed -0.123117 0.053118 -2.318 0.0209 *
## con_z 0.363198 0.051741 7.020 8.57e-12 ***
## ons_z -0.000589 0.052708 -0.011 0.9911
## sc_zed:con_z -0.032340 0.054870 -0.589 0.5559
## sc_zed:ons_z -0.012788 0.054044 -0.237 0.8131
## con_z:ons_z 0.124920 0.053539 2.333 0.0201 *
## sc_zed:con_z:ons_z -0.113404 0.055390 -2.047 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.072 on 436 degrees of freedom
## Multiple R-squared: 0.1353, Adjusted R-squared: 0.1214
## F-statistic: 9.748 on 7 and 436 DF, p-value: 2.678e-11sjPlot::plot_model(postermod, type = "int", mdrt.values = "meansd")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
posterplot <- ggpredict(postermod,
terms = c("sc_zed", "con_z [1,0,-1]",
"ons_z [1,0,-1]"),
ci.lvl = .95) %>%
mutate(scicur = x,
resp = predicted,
con = group,
ons = facet,
ons_labs = dplyr::recode(ons,
`1` = "Numeracy: z = 1",
`0` = "Numeracy: z = 0",
`-1` = "Numeracy: z = -1")) sjdmplot <- posterplot %>%
ggplot(aes(x = scicur, y = resp, color = con)) +
stat_smooth(method = "lm", fullrange = T, size = 2, fill = "lightgray") +
geom_ribbon(aes(x = scicur, ymin = conf.low, ymax = conf.high,
fill = con), alpha = .1) +
facet_wrap(~ons_labs,
ncol = 1) +
theme_bw() +
labs(x = "Science Curiosity (z-scored)",
y = "Predicted Change in Global Temperature",
color = "Conservativism (z-scored)") +
scale_color_manual(labels = c("Low", "Average", "High"),
values = c("blue", "black", "red", "white")) +
scale_fill_manual(values = c("blue", "black", "red", "white")) +
scale_x_discrete(limits = c(-2.5, -2, -1.5, -1, -0.5,
0, 0.5, 1, 1.5, 2, 2.5)) +
scale_y_discrete(limits = seq(from = 1, to = 4, by = .5)) +
theme(legend.position = "top",
axis.title = element_text(size = 50),
axis.text = element_text(size = 45),
legend.title = element_text(size = 45),
legend.text = element_text(size = 45),
strip.text = element_text(size = 45)) +
guides(fill = "none")
ggsave(filename = "sjdmplot.png", width = 20, height = 25, dpi = 300)sjdmplot <- posterplot %>%
ggplot(aes(x = scicur, y = resp, color = con)) +
stat_smooth(method = "lm", fullrange = T, size = 2, fill = "lightgray") +
geom_ribbon(aes(x = scicur, ymin = conf.low, ymax = conf.high,
fill = con), alpha = .1) +
facet_wrap(~ons_labs,
ncol = 1) +
theme_bw() +
labs(x = "Science Curiosity (z-scored)",
y = "Predicted Change in Arctic Sea Ice",
color = "Conservativism (z-scored)") +
scale_color_manual(labels = c("Low", "Average", "High"),
values = c("blue", "black", "red", "white")) +
scale_fill_manual(values = c("blue", "black", "red", "white")) +
scale_x_discrete(limits = c(-2.5, -2, -1.5, -1, -0.5,
0, 0.5, 1, 1.5, 2, 2.5),
expand = c(.01, .01)) +
scale_y_discrete(limits = seq(from = 1, to = 4, by = .5)) +
theme(legend.position = "top",
axis.title = element_text(size = 40),
axis.text = element_text(size = 35),
legend.title = element_text(size = 35),
legend.text = element_text(size = 35),
strip.text = element_text(size = 35)) +
guides(fill = "none")
ggsave(filename = "sjdmplot.png", width = 16, height = 22, dpi = 300)amodel1 <- lm(data = df4s, formula = arctic20 ~ con + rav_scored)
summary(amodel1)
amodel2 <- lm(data = df4s, formula = arctic20 ~ sc_scored + con + rav_scored)
summary(amodel2)Conservative #2: Temp
df4s %>%
ggplot(aes(x = temp20)) +
geom_histogram(binwidth = 1)
tempmod1 <- lm(ConTemp ~ 1, data = df4s)
summary(tempmod1)##
## Call:
## lm(formula = ConTemp ~ 1, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3471 -0.3471 -0.3471 0.6529 4.6529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.65291 0.04964 -33.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.146 on 532 degrees of freedomtempmod2 <- lm(ConTemp ~ rav_scoredz, data = df4s)
summary(tempmod2)##
## Call:
## lm(formula = ConTemp ~ rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6161 -0.4415 -0.3250 0.6168 4.6750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.65203 0.04953 -33.351 <2e-16 ***
## rav_scoredz -0.09302 0.05061 -1.838 0.0666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 531 degrees of freedom
## Multiple R-squared: 0.006323, Adjusted R-squared: 0.004451
## F-statistic: 3.379 on 1 and 531 DF, p-value: 0.0666anova(tempmod1, tempmod2)## Analysis of Variance Table
##
## Model 1: ConTemp ~ 1
## Model 2: ConTemp ~ rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 698.79
## 2 531 694.37 1 4.4182 3.3787 0.0666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tempmod3 <- lm(ConTemp ~ sc_scoredz + rav_scoredz, data = df4s)
summary(tempmod3)##
## Call:
## lm(formula = ConTemp ~ sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7689 -0.5174 -0.2900 0.5978 4.7220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.65277 0.04930 -33.523 <2e-16 ***
## sc_scoredz -0.12637 0.05148 -2.455 0.0144 *
## rav_scoredz -0.08236 0.05056 -1.629 0.1039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.138 on 530 degrees of freedom
## Multiple R-squared: 0.0175, Adjusted R-squared: 0.01379
## F-statistic: 4.719 on 2 and 530 DF, p-value: 0.009304anova(tempmod1, tempmod3)## Analysis of Variance Table
##
## Model 1: ConTemp ~ 1
## Model 2: ConTemp ~ sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 698.79
## 2 530 686.56 2 12.226 4.7188 0.009304 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tempmod4 <- lm(ConTemp ~ TempCong + sc_scoredz + rav_scoredz, data = df4s)
summary(tempmod4)##
## Call:
## lm(formula = ConTemp ~ TempCong + sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8364 -0.6130 -0.2471 0.6125 4.2125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7057288 0.0513271 -33.232 < 2e-16 ***
## TempCong 0.0005622 0.0001678 3.351 0.000863 ***
## sc_scoredz -0.1269027 0.0509863 -2.489 0.013118 *
## rav_scoredz -0.0781344 0.0500918 -1.560 0.119400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 529 degrees of freedom
## Multiple R-squared: 0.03792, Adjusted R-squared: 0.03246
## F-statistic: 6.95 on 3 and 529 DF, p-value: 0.0001358anova(tempmod3, tempmod4)## Analysis of Variance Table
##
## Model 1: ConTemp ~ sc_scoredz + rav_scoredz
## Model 2: ConTemp ~ TempCong + sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 530 686.56
## 2 529 672.29 1 14.27 11.229 0.0008629 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tmodel1 <- lm(data = df4s, formula = temp20 ~ con + rav_scored)
summary(tmodel1)##
## Call:
## lm(formula = temp20 ~ con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8841 -0.6924 -0.1105 0.4035 4.6465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.35887 0.17960 -7.566 1.72e-13 ***
## con 0.29092 0.05083 5.724 1.75e-08 ***
## rav_scored -0.04794 0.03082 -1.556 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.111 on 530 degrees of freedom
## Multiple R-squared: 0.06417, Adjusted R-squared: 0.06063
## F-statistic: 18.17 on 2 and 530 DF, p-value: 2.332e-08tmodel2 <- lm(data = df4s, formula = temp20 ~ sc_scored + con + rav_scored)
summary(tmodel2)##
## Call:
## lm(formula = temp20 ~ sc_scored + con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9977 -0.7322 -0.1271 0.4466 4.6851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.969683 0.263799 -3.676 0.000261 ***
## sc_scored -0.016956 0.008439 -2.009 0.045012 *
## con 0.281630 0.050895 5.534 4.94e-08 ***
## rav_scored -0.042931 0.030833 -1.392 0.164387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 529 degrees of freedom
## Multiple R-squared: 0.07125, Adjusted R-squared: 0.06599
## F-statistic: 13.53 on 3 and 529 DF, p-value: 1.621e-08Liberal #1: Ozone
df4s %>%
ggplot(aes(x = ozone20)) +
geom_histogram(binwidth = 1)
int_means_tab %>%
ggplot(aes(x = sc3, y = LibOzone_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Lib Ozone Item") +
geom_errorbar(aes(ymin=LibOzone_mean-LibOzone_se_f, ymax=LibOzone_mean+LibOzone_se_f), position = "dodge")
ozmod1 <- lm(LibOzone ~ 1, data = df4s)
summary(ozmod1)##
## Call:
## lm(formula = LibOzone ~ 1, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7167 -0.7167 -0.7167 1.2833 3.2833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28330 0.06086 -4.655 4.09e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 532 degrees of freedomozmod2 <- lm(LibOzone ~ rav_scoredz, data = df4s)
summary(ozmod2)##
## Call:
## lm(formula = LibOzone ~ rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8618 -0.7416 -0.6214 1.2584 3.3786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28270 0.06086 -4.645 4.29e-06 ***
## rav_scoredz -0.06402 0.06218 -1.030 0.304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 531 degrees of freedom
## Multiple R-squared: 0.001993, Adjusted R-squared: 0.0001131
## F-statistic: 1.06 on 1 and 531 DF, p-value: 0.3036anova(ozmod1, ozmod2)## Analysis of Variance Table
##
## Model 1: LibOzone ~ 1
## Model 2: LibOzone ~ rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1050.2
## 2 531 1048.1 1 2.0927 1.0602 0.3036ozmod3 <- lm(LibOzone ~ sc_scoredz + rav_scoredz, data = df4s)
summary(ozmod3)##
## Call:
## lm(formula = LibOzone ~ sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8531 -0.7784 -0.6067 1.2303 3.4422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28233 0.06086 -4.639 4.42e-06 ***
## sc_scoredz 0.06217 0.06354 0.978 0.328
## rav_scoredz -0.06927 0.06241 -1.110 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 530 degrees of freedom
## Multiple R-squared: 0.003792, Adjusted R-squared: 3.225e-05
## F-statistic: 1.009 on 2 and 530 DF, p-value: 0.3654anova(ozmod1, ozmod3)## Analysis of Variance Table
##
## Model 1: LibOzone ~ 1
## Model 2: LibOzone ~ sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1050.2
## 2 530 1046.2 2 3.9819 1.0086 0.3654ozmod4 <- lm(LibOzone ~ OzoneCong + sc_scoredz + rav_scoredz, data = df4s)
summary(ozmod4)##
## Call:
## lm(formula = LibOzone ~ OzoneCong + sc_scoredz + rav_scoredz,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8806 -0.7968 -0.6029 1.2086 3.4732
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2571049 0.0639370 -4.021 6.63e-05 ***
## OzoneCong -0.0002676 0.0002090 -1.280 0.201
## sc_scoredz 0.0624419 0.0635065 0.983 0.326
## rav_scoredz -0.0712639 0.0623921 -1.142 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 529 degrees of freedom
## Multiple R-squared: 0.006868, Adjusted R-squared: 0.001236
## F-statistic: 1.22 on 3 and 529 DF, p-value: 0.3019anova(ozmod1, ozmod4)## Analysis of Variance Table
##
## Model 1: LibOzone ~ 1
## Model 2: LibOzone ~ OzoneCong + sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1050.2
## 2 529 1043.0 3 7.2134 1.2195 0.3019omodel1 <- lm(data = df4s, formula = ozone20 ~ con + rav_scored)
summary(omodel1)##
## Call:
## lm(formula = ozone20 ~ con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9177 -0.7918 -0.6123 1.2082 3.4412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05196 0.22723 -0.229 0.819
## con -0.05359 0.06431 -0.833 0.405
## rav_scored -0.04196 0.03899 -1.076 0.282
##
## Residual standard error: 1.405 on 530 degrees of freedom
## Multiple R-squared: 0.003299, Adjusted R-squared: -0.0004625
## F-statistic: 0.877 on 2 and 530 DF, p-value: 0.4166omodel2 <- lm(data = df4s, formula = ozone20 ~ sc_scored + con + rav_scored)
summary(omodel2)##
## Call:
## lm(formula = ozone20 ~ sc_scored + con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8586 -0.7981 -0.5827 1.2080 3.3886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.274627 0.334764 -0.820 0.412
## sc_scored 0.009701 0.010709 0.906 0.365
## con -0.048274 0.064586 -0.747 0.455
## rav_scored -0.044829 0.039127 -1.146 0.252
##
## Residual standard error: 1.406 on 529 degrees of freedom
## Multiple R-squared: 0.004842, Adjusted R-squared: -0.0008011
## F-statistic: 0.858 on 3 and 529 DF, p-value: 0.4627Liberal #2: Air Quality
df4s %>%
ggplot(aes(x = airqual20)) +
geom_histogram(binwidth = 1)
int_means_tab %>%
ggplot(aes(x = sc3, y = LibAir_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Lib Air Item") +
geom_errorbar(aes(ymin=LibAir_mean-LibAir_se_f, ymax=LibAir_mean+LibAir_se_f), position = "dodge")
airmod1 <- lm(LibAir ~ 1, data = df4s)
summary(airmod1)##
## Call:
## lm(formula = LibAir ~ 1, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1557 -1.1557 -0.1557 0.8443 3.8443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.84428 0.06849 -12.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.581 on 532 degrees of freedomairmod2 <- lm(LibAir ~ rav_scoredz, data = df4s)
summary(airmod2)##
## Call:
## lm(formula = LibAir ~ rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5193 -1.1259 -0.2833 0.9528 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.84309 0.06835 -12.34 <2e-16 ***
## rav_scoredz -0.12570 0.06983 -1.80 0.0724 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.578 on 531 degrees of freedom
## Multiple R-squared: 0.006065, Adjusted R-squared: 0.004193
## F-statistic: 3.24 on 1 and 531 DF, p-value: 0.07241anova(airmod1, airmod2)## Analysis of Variance Table
##
## Model 1: LibAir ~ 1
## Model 2: LibAir ~ rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1330.1
## 2 531 1322.0 1 8.0673 3.2403 0.07241 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1airmod3 <- lm(LibAir ~ sc_scoredz + rav_scoredz, data = df4s)
summary(airmod3)##
## Call:
## lm(formula = LibAir ~ sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6277 -1.1527 -0.2882 1.0567 4.1005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.84361 0.06831 -12.349 <2e-16 ***
## sc_scoredz -0.08974 0.07132 -1.258 0.2089
## rav_scoredz -0.11813 0.07005 -1.686 0.0923 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.577 on 530 degrees of freedom
## Multiple R-squared: 0.009025, Adjusted R-squared: 0.005286
## F-statistic: 2.414 on 2 and 530 DF, p-value: 0.09048anova(airmod1, airmod3)## Analysis of Variance Table
##
## Model 1: LibAir ~ 1
## Model 2: LibAir ~ sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1330.1
## 2 530 1318.1 2 12.004 2.4135 0.09048 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1airmod4 <- lm(LibAir ~ AirCong + sc_scoredz + rav_scoredz, data = df4s)
summary(airmod4)##
## Call:
## lm(formula = LibAir ~ AirCong + sc_scoredz + rav_scoredz, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6687 -1.1710 -0.3216 1.1310 4.3537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8098036 0.0717166 -11.292 <2e-16 ***
## AirCong -0.0003586 0.0002344 -1.530 0.1267
## sc_scoredz -0.0893699 0.0712337 -1.255 0.2102
## rav_scoredz -0.1208028 0.0699837 -1.726 0.0849 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.575 on 529 degrees of freedom
## Multiple R-squared: 0.01339, Adjusted R-squared: 0.007795
## F-statistic: 2.393 on 3 and 529 DF, p-value: 0.06764anova(airmod1, airmod4)## Analysis of Variance Table
##
## Model 1: LibAir ~ 1
## Model 2: LibAir ~ AirCong + sc_scoredz + rav_scoredz
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 532 1330.1
## 2 529 1312.3 3 17.809 2.3931 0.06764 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aqmodel1 <- lm(data = df4s, formula = airqual20 ~ con + rav_scored)
summary(aqmodel1)##
## Call:
## lm(formula = airqual20 ~ con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5007 -1.1441 -0.2626 0.9554 4.0348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.39976 0.25535 -1.566 0.1180
## con -0.02013 0.07226 -0.279 0.7806
## rav_scored -0.07938 0.04382 -1.812 0.0706 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.579 on 530 degrees of freedom
## Multiple R-squared: 0.006211, Adjusted R-squared: 0.002461
## F-statistic: 1.656 on 2 and 530 DF, p-value: 0.1919aqmodel2 <- lm(data = df4s, formula = airqual20 ~ sc_scored + con + rav_scored)
summary(aqmodel2)##
## Call:
## lm(formula = airqual20 ~ sc_scored + con + rav_scored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6045 -1.1586 -0.2898 1.0349 4.0787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04432 0.37589 -0.118 0.9062
## sc_scored -0.01549 0.01202 -1.288 0.1983
## con -0.02862 0.07252 -0.395 0.6932
## rav_scored -0.07481 0.04393 -1.703 0.0892 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.578 on 529 degrees of freedom
## Multiple R-squared: 0.009317, Adjusted R-squared: 0.003699
## F-statistic: 1.658 on 3 and 529 DF, p-value: 0.175int_means_fill <- df4s %>%
group_by(sc3, ideo2) %>%
summarise_at(vars("FillBacteria", "FillQuake", "ConFillCO2", "ConFillIce"), funs(mean, se_f, length))
int_means_tab_fill <- int_means_fill %>%
select(-contains("length")) %>%
select(sc3, ideo2, contains("Fill"))CO2 Filler
int_means_tab_fill %>%
ggplot(aes(x = sc3, y = ConFillCO2_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Con Fill CO2 Item") +
geom_errorbar(aes(ymin=ConFillCO2_mean-ConFillCO2_se_f, ymax=ConFillCO2_mean+ConFillCO2_se_f), position = "dodge")
Ice Filler
int_means_tab_fill %>%
ggplot(aes(x = sc3, y = ConFillIce_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Con Fill CO2 Item") +
geom_errorbar(aes(ymin=ConFillIce_mean-ConFillIce_se_f, ymax=ConFillIce_mean+ConFillIce_se_f), position = "dodge")
Bacteria Filler
int_means_tab_fill %>%
ggplot(aes(x = sc3, y = FillBacteria_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Fill Bacteria Item") +
geom_errorbar(aes(ymin=FillBacteria_mean-FillBacteria_se_f, ymax=FillBacteria_mean+FillBacteria_se_f), position = "dodge")
Quake Filler
int_means_tab_fill %>%
ggplot(aes(x = sc3, y = FillQuake_mean, fill = ideo2)) +
geom_bar(stat = "identity", position = "dodge") +
# scale_x_discrete(labels = c("Con", "Lib", "Mod")) +
scale_fill_manual(values = c("brown3", "deepskyblue3", "darkmagenta")) +
theme_bw() +
labs(x = "Science Curiosity", fill = "Ideology", y = "Weighting Local Trend", title = "Fill Quake Item") +
geom_errorbar(aes(ymin=FillQuake_mean-FillQuake_se_f, ymax=FillQuake_mean+FillQuake_se_f), position = "dodge")
Preregistered Analyses
MLM (H1)
attempt 2
dfmlm1 <- df4s %>%
pivot_longer(., cols = c(arctic20_ch, temp20_ch, co220_ch, icesheets20_ch),
names_to = "item",
values_to = "response",
values_drop_na = T)dfmlm2 <- dfmlm1 %>%
select(id, con, conservatism, item, response, sc_scored, sc_scoredz,
num_score_b, rav_scored, rav_scoredz) %>%
mutate(item_slope = factor(case_when(
item == "arctic20_ch" | item == "ice_sheets20_ch" ~ "neg",
item == "temp20_ch" | item == "co220_ch" ~ "pos"
)),
item_ambiguous = factor(case_when(
item == "arctic20_ch" | item == "temp20_ch" ~ "1",
item == "co220_ch" | item == "ice_sheets20_ch" ~ "0"
)),
item = factor(item),
response = as.numeric(response),
id = factor(id),
conservatism = as.numeric(conservatism),
sc_zscored = as.numeric(sc_scoredz),
rav_zscored = as.numeric(rav_scoredz),
ons_zscored = as.numeric(scale(num_score_b)),
con_z = as.numeric(scale(conservatism))
)int_model1 <- lmer(response ~ 1 + (1 | item), data = dfmlm2)
summary(int_model1)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + (1 | item)
## Data: dfmlm2
##
## REML criterion at convergence: 6701.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3452 -0.7879 -0.2958 0.3779 4.3784
##
## Random effects:
## Groups Name Variance Std.Dev.
## item (Intercept) 0.1062 0.3258
## Residual 1.3472 1.1607
## Number of obs: 2132, groups: item, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.1843 0.1648 13.25int_model2 <- lmer(response ~ 1 + (1 | item) + (1 | id), data = dfmlm2)
summary(int_model2)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + (1 | item) + (1 | id)
## Data: dfmlm2
##
## REML criterion at convergence: 6200.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9578 -0.4969 -0.0477 0.3325 5.6287
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.6069 0.7790
## item (Intercept) 0.1073 0.3276
## Residual 0.7403 0.8604
## Number of obs: 2132, groups: id, 533; item, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.1843 0.1683 12.98anova(int_model1, int_model2)## Data: dfmlm2
## Models:
## int_model1: response ~ 1 + (1 | item)
## int_model2: response ~ 1 + (1 | item) + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## int_model1 3 6705.6 6722.6 -3349.8 6699.6
## int_model2 4 6206.9 6229.6 -3099.5 6198.9 500.67 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model3 <- lmer(response ~ 1 + conservatism_f + (1 + conservatism_f | item) +
(1 + conservatism_f | id), data = dfmlm2)
summary(model3)
anova(int_model2, model3)attempt 3
int_model1 <- lmer(response ~ 1 + (1 | item), data = dfmlm2)
summary(int_model1)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + (1 | item)
## Data: dfmlm2
##
## REML criterion at convergence: 6701.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3452 -0.7879 -0.2958 0.3779 4.3784
##
## Random effects:
## Groups Name Variance Std.Dev.
## item (Intercept) 0.1062 0.3258
## Residual 1.3472 1.1607
## Number of obs: 2132, groups: item, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.1843 0.1648 13.25int_model2 <- lmer(response ~ 1 + (1 | item) + (1 | id), data = dfmlm2)
summary(int_model2)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + (1 | item) + (1 | id)
## Data: dfmlm2
##
## REML criterion at convergence: 6200.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9578 -0.4969 -0.0477 0.3325 5.6287
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.6069 0.7790
## item (Intercept) 0.1073 0.3276
## Residual 0.7403 0.8604
## Number of obs: 2132, groups: id, 533; item, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.1843 0.1683 12.98anova(int_model1, int_model2)## Data: dfmlm2
## Models:
## int_model1: response ~ 1 + (1 | item)
## int_model2: response ~ 1 + (1 | item) + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## int_model1 3 6705.6 6722.6 -3349.8 6699.6
## int_model2 4 6206.9 6229.6 -3099.5 6198.9 500.67 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model3 <- lmer(response ~ 1 + conservatism_f + (1 + conservatism_f | item) +
(1 + conservatism_f | id), data = dfmlm2)
summary(model3)
anova(int_model2, model3)
model4 <- lmer(response ~ 1 + conservatism_f +
(1 | item) +
(1 + conservatism_f | id), data = dfmlm2)
summary(model4)
anova(int_model2, model4)
model5 <- lmer(response ~ 1 + conservatism_f + sc_zscored +
(1 | item) +
(1 + conservatism_f + sc_zscored | id), data = dfmlm2)
summary(model5)
anova(model4, model5)
model6 <- lmer(response ~ 1 + conservatism_f + rav_zscored +
(1 | item) +
(1 + conservatism_f + rav_zscored | id), data = dfmlm2)
summary(model6)
anova(model4, model6)
model7 <- lmer(response ~ 1 + conservatism_f + rav_zscored + aot_zscored +
(1 | item) +
(1 + conservatism_f + rav_zscored + aot_zscored| id), data = dfmlm2)
summary(model7)
anova(model6, model7)model7 <- lmer(response ~ 1 + conservatism * sc_scoredz +
(1 | item) +
(1 | id), data = dfmlm2)
summary(model7)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + conservatism * sc_scoredz + (1 | item) + (1 | id)
## Data: dfmlm2
##
## REML criterion at convergence: 6161.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8716 -0.5656 -0.1271 0.3531 5.7214
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.5321 0.7295
## item (Intercept) 0.1073 0.3276
## Residual 0.7403 0.8604
## Number of obs: 2132, groups: id, 533; item, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.627917 0.184053 8.845
## conservatism 0.151779 0.020696 7.334
## sc_scoredz 0.008479 0.091480 0.093
## conservatism:sc_scoredz -0.014994 0.022766 -0.659
##
## Correlation of Fixed Effects:
## (Intr) cnsrvt sc_scr
## conservatsm -0.410
## sc_scoredz -0.038 0.040
## cnsrvtsm:s_ 0.021 0.008 -0.908dfmlm3 <- dfmlm2 %>%
filter(conservatism != 4)
model8 <- lmer(response ~ 1 + con_z * sc_scoredz * ons_zscored +
item_ambiguous +
(1 | item) +
(1 | id), data = dfmlm3)
summary(model8)## Linear mixed model fit by REML ['lmerMod']
## Formula: response ~ 1 + con_z * sc_scoredz * ons_zscored + item_ambiguous +
## (1 | item) + (1 | id)
## Data: dfmlm3
##
## REML criterion at convergence: 3942.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2695 -0.5549 -0.1064 0.3875 4.3673
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.45507 0.6746
## item (Intercept) 0.02781 0.1668
## Residual 0.78746 0.8874
## Number of obs: 1332, groups: id, 444; item, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.868527 0.175110 10.671
## con_z 0.272321 0.037418 7.278
## sc_scoredz -0.079182 0.044692 -1.772
## ons_zscored 0.020858 0.041663 0.501
## item_ambiguous1 0.563063 0.210655 2.673
## con_z:sc_scoredz -0.042198 0.042157 -1.001
## con_z:ons_zscored 0.093213 0.038524 2.420
## sc_scoredz:ons_zscored -0.004001 0.045399 -0.088
## con_z:sc_scoredz:ons_zscored -0.067089 0.042417 -1.582
##
## Correlation of Fixed Effects:
## (Intr) con_z sc_scr ons_zs itm_m1 cn_z:s_ cn_z:n_ sc_s:_
## con_z 0.008
## sc_scoredz 0.004 0.123
## ons_zscored 0.005 0.079 -0.020
## item_ambgs1 -0.802 0.000 0.000 0.000
## cn_z:sc_scr 0.029 0.033 0.091 0.088 0.000
## cn_z:ns_zsc 0.018 0.029 0.089 0.172 0.000 -0.009
## sc_scrdz:n_ -0.010 0.083 0.170 0.089 0.000 0.192 0.187
## cn_z:sc_s:_ 0.018 -0.026 0.190 0.186 0.000 0.146 0.089 0.132“IMRI” (H2)
df4s <- df4s %>%
mutate(ebiasz3_arctic = ifelse(arctic20_z >= 3, 1, 0),
ebiasz3_temp = ifelse(temp20_z >= 3, 1, 0),
ebiasz3_ozone = ifelse(ozone20_z >= 3, 1, 0),
ebiasz3_airqual = ifelse(airqual20_z >= 3, 1, 0))
df4s %>%
select(contains("ebiasz3")) %>%
psych::describe()## vars n mean sd median trimmed mad min max range skew
## ebiasz3_arctic 1 533 0.01 0.07 0 0 0 0 1 1 13.18
## ebiasz3_temp 2 533 0.03 0.16 0 0 0 0 1 1 5.91
## ebiasz3_ozone 3 533 0.00 0.00 0 0 0 0 0 0 NaN
## ebiasz3_airqual 4 533 0.00 0.00 0 0 0 0 0 0 NaN
## kurtosis se
## ebiasz3_arctic 172.01 0.00
## ebiasz3_temp 32.96 0.01
## ebiasz3_ozone NaN 0.00
## ebiasz3_airqual NaN 0.00arcticmodel1 <- glm(data = df4s, formula = ebiasz3_arctic ~ conservatism_f + rav_scored, family = "binomial")
summary(arcticmodel1)
arcticmodel2 <- glm(data = df4s, formula = ebiasz3_arctic ~ sc_scored + conservatism_f + rav_scored, family = "binomial")
summary(arcticmodel2)tempmodel1 <- glm(data = df4s, formula = ebiasz3_temp ~ conservatism_f + rav_scored, family = "binomial")
summary(tempmodel1)
tempmodel2 <- glm(data = df4s, formula = ebiasz3_temp ~ sc_scored + conservatism_f + rav_scored, family = "binomial")
summary(tempmodel2)ozonemodel1 <- glm(data = df4s, formula = ebiasz3_ozone ~ conservatism_f + rav_scored, family = "binomial")
summary(ozonemodel1)
ozonemodel2 <- glm(data = df4s, formula = ebiasz3_ozone ~ sc_scored + conservatism_f + rav_scored, family = "binomial")
summary(ozonemodel2)airqualmodel1 <- glm(data = df4s, formula = ebiasz3_airqual ~ conservatism_f + rav_scored, family = "binomial")
summary(airqualmodel1)
airqualmodel2 <- glm(data = df4s, formula = ebiasz3_airqual ~ sc_scored + conservatism_f + rav_scored, family = "binomial")
summary(airqualmodel2)Exploratory Analyses
tons of aot/nfc models
look1 <- lm(data = df4s, formula = arctic20_ch ~ conservatism_f + rav_scoredz + sc_scoredz + aot_zscored + nfc_zscored)
summary(look1)##
## Call:
## lm(formula = arctic20_ch ~ conservatism_f + rav_scoredz + sc_scoredz +
## aot_zscored + nfc_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6814 -0.8452 -0.2202 0.4617 4.1234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.027250 0.130492 15.535 < 2e-16 ***
## conservatism_f 0.149065 0.032094 4.645 4.31e-06 ***
## rav_scoredz -0.007998 0.057539 -0.139 0.889507
## sc_scoredz -0.057148 0.059741 -0.957 0.339210
## aot_zscored -0.214203 0.064277 -3.332 0.000921 ***
## nfc_zscored 0.107467 0.058797 1.828 0.068149 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.247 on 527 degrees of freedom
## Multiple R-squared: 0.08962, Adjusted R-squared: 0.08098
## F-statistic: 10.38 on 5 and 527 DF, p-value: 1.658e-09look2 <- lm(data = df4s, formula = arctic20_ch ~ conservatism_f * aot_zscored)
summary(look2)##
## Call:
## lm(formula = arctic20_ch ~ conservatism_f * aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0670 -0.8532 -0.1803 0.3755 4.0770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07431 0.13099 15.836 < 2e-16 ***
## conservatism_f 0.14929 0.03180 4.695 3.4e-06 ***
## aot_zscored -0.51006 0.13256 -3.848 0.000134 ***
## conservatism_f:aot_zscored 0.08260 0.03088 2.675 0.007702 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 529 degrees of freedom
## Multiple R-squared: 0.09574, Adjusted R-squared: 0.09061
## F-statistic: 18.67 on 3 and 529 DF, p-value: 1.592e-11look23 <- lm(data = df4s, formula = arctic20_ch ~ conservatism_f * aot_zscored)
summary(look23)##
## Call:
## lm(formula = arctic20_ch ~ conservatism_f * aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0670 -0.8532 -0.1803 0.3755 4.0770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07431 0.13099 15.836 < 2e-16 ***
## conservatism_f 0.14929 0.03180 4.695 3.4e-06 ***
## aot_zscored -0.51006 0.13256 -3.848 0.000134 ***
## conservatism_f:aot_zscored 0.08260 0.03088 2.675 0.007702 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 529 degrees of freedom
## Multiple R-squared: 0.09574, Adjusted R-squared: 0.09061
## F-statistic: 18.67 on 3 and 529 DF, p-value: 1.592e-11look3 <- lm(data = df4s, formula = arctic20_ch ~ rav_scoredz + conservatism_f * aot_zscored)
summary(look3)##
## Call:
## lm(formula = arctic20_ch ~ rav_scoredz + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0780 -0.8487 -0.1807 0.3804 4.0855
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.074559 0.131140 15.819 < 2e-16 ***
## rav_scoredz 0.004951 0.057429 0.086 0.931339
## conservatism_f 0.149267 0.031829 4.690 3.49e-06 ***
## aot_zscored -0.512460 0.135573 -3.780 0.000175 ***
## conservatism_f:aot_zscored 0.082851 0.031047 2.669 0.007851 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.242 on 528 degrees of freedom
## Multiple R-squared: 0.09575, Adjusted R-squared: 0.0889
## F-statistic: 13.98 on 4 and 528 DF, p-value: 7.574e-11look4 <- lm(data = df4s, formula = arctic20_ch ~ vl_zscored + conservatism_f * aot_zscored)
summary(look4)##
## Call:
## lm(formula = arctic20_ch ~ vl_zscored + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0754 -0.8694 -0.1464 0.3857 4.1526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07627 0.13103 15.846 < 2e-16 ***
## vl_zscored -0.04935 0.05541 -0.891 0.373489
## conservatism_f 0.14856 0.03181 4.670 3.83e-06 ***
## aot_zscored -0.49418 0.13378 -3.694 0.000244 ***
## conservatism_f:aot_zscored 0.08104 0.03093 2.620 0.009044 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 528 degrees of freedom
## Multiple R-squared: 0.0971, Adjusted R-squared: 0.09026
## F-statistic: 14.2 on 4 and 528 DF, p-value: 5.184e-11look5 <- lm(data = df4s, formula = arctic20_ch ~ nfc_zscored
+ conservatism_f * aot_zscored) # 3 way int is a no go tho, w none of nfc int sig but me is
summary(look5)##
## Call:
## lm(formula = arctic20_ch ~ nfc_zscored + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2124 -0.8266 -0.1772 0.3910 3.9885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07519 0.13068 15.880 < 2e-16 ***
## nfc_zscored 0.10384 0.05566 1.865 0.06267 .
## conservatism_f 0.14989 0.03173 4.725 2.96e-06 ***
## aot_zscored -0.55419 0.13435 -4.125 4.31e-05 ***
## conservatism_f:aot_zscored 0.08775 0.03093 2.837 0.00473 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.238 on 528 degrees of freedom
## Multiple R-squared: 0.1017, Adjusted R-squared: 0.09486
## F-statistic: 14.94 on 4 and 528 DF, p-value: 1.422e-11look6 <- lm(data = df4s, formula = arctic20_ch ~ sc_scoredz
+ conservatism_f * aot_zscored)
summary(look6)##
## Call:
## lm(formula = arctic20_ch ~ sc_scoredz + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0484 -0.8521 -0.1759 0.3848 4.0835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07839 0.13147 15.808 < 2e-16 ***
## sc_scoredz -0.02292 0.05640 -0.406 0.684657
## conservatism_f 0.14813 0.03195 4.636 4.48e-06 ***
## aot_zscored -0.50852 0.13272 -3.832 0.000143 ***
## conservatism_f:aot_zscored 0.08253 0.03090 2.671 0.007798 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.242 on 528 degrees of freedom
## Multiple R-squared: 0.09602, Adjusted R-squared: 0.08918
## F-statistic: 14.02 on 4 and 528 DF, p-value: 7.019e-11look7 <- lm(data = df4s, formula = arctic20_ch ~ sc_scoredz + nfc_zscored
+ conservatism_f * aot_zscored)
summary(look7)##
## Call:
## lm(formula = arctic20_ch ~ sc_scoredz + nfc_zscored + conservatism_f *
## aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1805 -0.8272 -0.1734 0.4039 4.0080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.08635 0.13111 15.913 < 2e-16 ***
## sc_scoredz -0.06175 0.05918 -1.043 0.29723
## nfc_zscored 0.12296 0.05860 2.098 0.03635 *
## conservatism_f 0.14687 0.03185 4.611 5.04e-06 ***
## aot_zscored -0.55816 0.13439 -4.153 3.82e-05 ***
## conservatism_f:aot_zscored 0.08852 0.03093 2.862 0.00438 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.238 on 527 degrees of freedom
## Multiple R-squared: 0.1035, Adjusted R-squared: 0.09501
## F-statistic: 12.17 on 5 and 527 DF, p-value: 3.544e-11look8 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f + aot_zscored + rav_scoredz)
summary(look8)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f + aot_zscored + rav_scoredz,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.74709 -0.60394 0.01412 0.68566 2.63830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18081 0.09962 1.815 0.0701 .
## conservatism_f -0.05097 0.02448 -2.083 0.0378 *
## aot_zscored 0.03770 0.04821 0.782 0.4346
## rav_scoredz 0.06638 0.04397 1.510 0.1318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9553 on 529 degrees of freedom
## Multiple R-squared: 0.01987, Adjusted R-squared: 0.01431
## F-statistic: 3.574 on 3 and 529 DF, p-value: 0.01394look9 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f)
summary(look9)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.72928 -0.60925 0.01553 0.68654 2.59259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21865 0.09455 2.313 0.02113 *
## conservatism_f -0.06076 0.02308 -2.633 0.00872 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9569 on 531 degrees of freedom
## Multiple R-squared: 0.01288, Adjusted R-squared: 0.01102
## F-statistic: 6.931 on 1 and 531 DF, p-value: 0.00872look10 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f + aot_zscored)
summary(look10)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f + aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.67766 -0.62788 0.00906 0.69085 2.62407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17979 0.09974 1.803 0.0720 .
## conservatism_f -0.05068 0.02450 -2.068 0.0391 *
## aot_zscored 0.05679 0.04658 1.219 0.2233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9564 on 530 degrees of freedom
## Multiple R-squared: 0.01564, Adjusted R-squared: 0.01193
## F-statistic: 4.212 on 2 and 530 DF, p-value: 0.01532look11 <- lm(data = df4s, formula = sc_scoredz ~ aot_zscored)
summary(look11)##
## Call:
## lm(formula = sc_scoredz ~ aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57256 -0.66113 0.04375 0.64862 2.62779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007838 0.041577 -0.189 0.8505
## aot_zscored 0.089279 0.043985 2.030 0.0429 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9594 on 531 degrees of freedom
## Multiple R-squared: 0.007699, Adjusted R-squared: 0.00583
## F-statistic: 4.12 on 1 and 531 DF, p-value: 0.04288look1 <- lm(data = df4s, formula = arctic20_ch ~ conservatism_f + rav_scoredz + sc_scoredz + aot_zscored + nfc_zscored)
summary(look1)##
## Call:
## lm(formula = arctic20_ch ~ conservatism_f + rav_scoredz + sc_scoredz +
## aot_zscored + nfc_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6814 -0.8452 -0.2202 0.4617 4.1234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.027250 0.130492 15.535 < 2e-16 ***
## conservatism_f 0.149065 0.032094 4.645 4.31e-06 ***
## rav_scoredz -0.007998 0.057539 -0.139 0.889507
## sc_scoredz -0.057148 0.059741 -0.957 0.339210
## aot_zscored -0.214203 0.064277 -3.332 0.000921 ***
## nfc_zscored 0.107467 0.058797 1.828 0.068149 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.247 on 527 degrees of freedom
## Multiple R-squared: 0.08962, Adjusted R-squared: 0.08098
## F-statistic: 10.38 on 5 and 527 DF, p-value: 1.658e-09look2 <- lm(data = df4s, formula = arctic20_ch ~ conservatism_f * aot_zscored)
summary(look2)##
## Call:
## lm(formula = arctic20_ch ~ conservatism_f * aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0670 -0.8532 -0.1803 0.3755 4.0770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07431 0.13099 15.836 < 2e-16 ***
## conservatism_f 0.14929 0.03180 4.695 3.4e-06 ***
## aot_zscored -0.51006 0.13256 -3.848 0.000134 ***
## conservatism_f:aot_zscored 0.08260 0.03088 2.675 0.007702 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 529 degrees of freedom
## Multiple R-squared: 0.09574, Adjusted R-squared: 0.09061
## F-statistic: 18.67 on 3 and 529 DF, p-value: 1.592e-11look3 <- lm(data = df4s, formula = arctic20_ch ~ rav_scoredz + conservatism_f * aot_zscored)
summary(look3)##
## Call:
## lm(formula = arctic20_ch ~ rav_scoredz + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0780 -0.8487 -0.1807 0.3804 4.0855
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.074559 0.131140 15.819 < 2e-16 ***
## rav_scoredz 0.004951 0.057429 0.086 0.931339
## conservatism_f 0.149267 0.031829 4.690 3.49e-06 ***
## aot_zscored -0.512460 0.135573 -3.780 0.000175 ***
## conservatism_f:aot_zscored 0.082851 0.031047 2.669 0.007851 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.242 on 528 degrees of freedom
## Multiple R-squared: 0.09575, Adjusted R-squared: 0.0889
## F-statistic: 13.98 on 4 and 528 DF, p-value: 7.574e-11look4 <- lm(data = df4s, formula = arctic20_ch ~ vl_zscored + conservatism_f * aot_zscored)
summary(look4)##
## Call:
## lm(formula = arctic20_ch ~ vl_zscored + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0754 -0.8694 -0.1464 0.3857 4.1526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07627 0.13103 15.846 < 2e-16 ***
## vl_zscored -0.04935 0.05541 -0.891 0.373489
## conservatism_f 0.14856 0.03181 4.670 3.83e-06 ***
## aot_zscored -0.49418 0.13378 -3.694 0.000244 ***
## conservatism_f:aot_zscored 0.08104 0.03093 2.620 0.009044 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 528 degrees of freedom
## Multiple R-squared: 0.0971, Adjusted R-squared: 0.09026
## F-statistic: 14.2 on 4 and 528 DF, p-value: 5.184e-11look5 <- lm(data = df4s, formula = arctic20_ch ~ nfc_zscored
+ conservatism_f * aot_zscored) # 3 way int is a no go tho, w none of
# nfc int sig but me is
summary(look5)##
## Call:
## lm(formula = arctic20_ch ~ nfc_zscored + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2124 -0.8266 -0.1772 0.3910 3.9885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07519 0.13068 15.880 < 2e-16 ***
## nfc_zscored 0.10384 0.05566 1.865 0.06267 .
## conservatism_f 0.14989 0.03173 4.725 2.96e-06 ***
## aot_zscored -0.55419 0.13435 -4.125 4.31e-05 ***
## conservatism_f:aot_zscored 0.08775 0.03093 2.837 0.00473 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.238 on 528 degrees of freedom
## Multiple R-squared: 0.1017, Adjusted R-squared: 0.09486
## F-statistic: 14.94 on 4 and 528 DF, p-value: 1.422e-11look6 <- lm(data = df4s, formula = arctic20_ch ~ sc_scoredz
+ conservatism_f * aot_zscored)
summary(look6)##
## Call:
## lm(formula = arctic20_ch ~ sc_scoredz + conservatism_f * aot_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0484 -0.8521 -0.1759 0.3848 4.0835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07839 0.13147 15.808 < 2e-16 ***
## sc_scoredz -0.02292 0.05640 -0.406 0.684657
## conservatism_f 0.14813 0.03195 4.636 4.48e-06 ***
## aot_zscored -0.50852 0.13272 -3.832 0.000143 ***
## conservatism_f:aot_zscored 0.08253 0.03090 2.671 0.007798 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.242 on 528 degrees of freedom
## Multiple R-squared: 0.09602, Adjusted R-squared: 0.08918
## F-statistic: 14.02 on 4 and 528 DF, p-value: 7.019e-11look7 <- lm(data = df4s, formula = arctic20_ch ~ sc_scoredz + nfc_zscored
+ conservatism_f * aot_zscored)
summary(look7)##
## Call:
## lm(formula = arctic20_ch ~ sc_scoredz + nfc_zscored + conservatism_f *
## aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1805 -0.8272 -0.1734 0.4039 4.0080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.08635 0.13111 15.913 < 2e-16 ***
## sc_scoredz -0.06175 0.05918 -1.043 0.29723
## nfc_zscored 0.12296 0.05860 2.098 0.03635 *
## conservatism_f 0.14687 0.03185 4.611 5.04e-06 ***
## aot_zscored -0.55816 0.13439 -4.153 3.82e-05 ***
## conservatism_f:aot_zscored 0.08852 0.03093 2.862 0.00438 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.238 on 527 degrees of freedom
## Multiple R-squared: 0.1035, Adjusted R-squared: 0.09501
## F-statistic: 12.17 on 5 and 527 DF, p-value: 3.544e-11look8 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f + aot_zscored + rav_scoredz)
summary(look8)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f + aot_zscored + rav_scoredz,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.74709 -0.60394 0.01412 0.68566 2.63830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18081 0.09962 1.815 0.0701 .
## conservatism_f -0.05097 0.02448 -2.083 0.0378 *
## aot_zscored 0.03770 0.04821 0.782 0.4346
## rav_scoredz 0.06638 0.04397 1.510 0.1318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9553 on 529 degrees of freedom
## Multiple R-squared: 0.01987, Adjusted R-squared: 0.01431
## F-statistic: 3.574 on 3 and 529 DF, p-value: 0.01394look9 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f)
summary(look9)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.72928 -0.60925 0.01553 0.68654 2.59259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21865 0.09455 2.313 0.02113 *
## conservatism_f -0.06076 0.02308 -2.633 0.00872 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9569 on 531 degrees of freedom
## Multiple R-squared: 0.01288, Adjusted R-squared: 0.01102
## F-statistic: 6.931 on 1 and 531 DF, p-value: 0.00872look10 <- lm(data = df4s, formula = sc_scoredz ~ conservatism_f + aot_zscored)
summary(look10)##
## Call:
## lm(formula = sc_scoredz ~ conservatism_f + aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.67766 -0.62788 0.00906 0.69085 2.62407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17979 0.09974 1.803 0.0720 .
## conservatism_f -0.05068 0.02450 -2.068 0.0391 *
## aot_zscored 0.05679 0.04658 1.219 0.2233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9564 on 530 degrees of freedom
## Multiple R-squared: 0.01564, Adjusted R-squared: 0.01193
## F-statistic: 4.212 on 2 and 530 DF, p-value: 0.01532look11 <- lm(data = df4s, formula = sc_scoredz ~ aot_zscored)
summary(look11)##
## Call:
## lm(formula = sc_scoredz ~ aot_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57256 -0.66113 0.04375 0.64862 2.62779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007838 0.041577 -0.189 0.8505
## aot_zscored 0.089279 0.043985 2.030 0.0429 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9594 on 531 degrees of freedom
## Multiple R-squared: 0.007699, Adjusted R-squared: 0.00583
## F-statistic: 4.12 on 1 and 531 DF, p-value: 0.04288look12 <- lm(data = df4s, formula = sc_scoredz ~ vl_zscored + rav_scoredz + nfc_zscored)
summary(look12)##
## Call:
## lm(formula = sc_scoredz ~ vl_zscored + rav_scoredz + nfc_zscored,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.52630 -0.61392 0.00016 0.62678 2.44010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007654 0.039485 -0.194 0.846
## vl_zscored 0.009026 0.041302 0.219 0.827
## rav_scoredz 0.057045 0.041761 1.366 0.173
## nfc_zscored 0.306424 0.040043 7.652 9.4e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9115 on 529 degrees of freedom
## Multiple R-squared: 0.1076, Adjusted R-squared: 0.1026
## F-statistic: 21.26 on 3 and 529 DF, p-value: 5.106e-13look13 <- lm(data = df4s, formula = sc_scoredz ~ rav_scoredz +
conservatism_f + nfc_zscored + aot_zscored + vl_zscored)
summary(look13)##
## Call:
## lm(formula = sc_scoredz ~ rav_scoredz + conservatism_f + nfc_zscored +
## aot_zscored + vl_zscored, data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6073 -0.6264 0.0049 0.6361 2.4335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.172842 0.094882 1.822 0.0691 .
## rav_scoredz 0.058887 0.042823 1.375 0.1697
## conservatism_f -0.048736 0.023314 -2.090 0.0371 *
## nfc_zscored 0.306059 0.040821 7.498 2.77e-13 ***
## aot_zscored -0.033601 0.047131 -0.713 0.4762
## vl_zscored 0.006859 0.041554 0.165 0.8690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9095 on 527 degrees of freedom
## Multiple R-squared: 0.115, Adjusted R-squared: 0.1066
## F-statistic: 13.69 on 5 and 527 DF, p-value: 1.397e-12plots
df4s %>%
ggplot(aes(x = aot_zscored, y = sc_scoredz, fill = as.factor(con))) +
geom_smooth()
df4s %>%
ggplot(aes(x = aot_zscored, y = sc_scoredz)) +
geom_smooth()
df4s %>%
ggplot(aes(x = con, y = sc_scoredz)) +
geom_smooth()
expmods
expmod1 <- lm(data = df3s, formula = arctic10_ch ~ rav_scoredz + con + sc_scoredz)
summary(expmod1)##
## Call:
## lm(formula = arctic10_ch ~ rav_scoredz + con + sc_scoredz, data = df3s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4114 -0.4028 -0.0401 0.1636 3.9250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12152 0.03950 79.020 < 2e-16 ***
## rav_scoredz -0.05683 0.04040 -1.407 0.160
## con 0.18909 0.04174 4.531 7.27e-06 ***
## sc_scoredz -0.01027 0.04125 -0.249 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9083 on 529 degrees of freedom
## Multiple R-squared: 0.04316, Adjusted R-squared: 0.03773
## F-statistic: 7.954 on 3 and 529 DF, p-value: 3.397e-05expmod2 <- lm(data = df3s, formula = arctic20_ch ~ rav_scoredz + con + sc_scoredz)
summary(expmod2)##
## Call:
## lm(formula = arctic20_ch ~ rav_scoredz + con + sc_scoredz, data = df3s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1408 -0.9026 -0.2549 0.5698 4.6022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.59650 0.05516 47.074 < 2e-16 ***
## rav_scoredz -0.06392 0.05641 -1.133 0.258
## con 0.30482 0.05828 5.231 2.44e-07 ***
## sc_scoredz -0.03785 0.05760 -0.657 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.268 on 529 degrees of freedom
## Multiple R-squared: 0.0552, Adjusted R-squared: 0.04985
## F-statistic: 10.3 on 3 and 529 DF, p-value: 1.335e-06plots
df4s %>%
ggplot(aes(x = aot_zscored, y = arctic20_ch, fill = as.factor(con))) +
geom_smooth()
df4s %>%
ggplot(aes(x = con, y = arctic20_ch)) +
geom_bar(fun = "mean", stat = "summary")
df4s %>%
filter(con == -1) %>%
select(aot_zscored) %>%
hist()
df4s %>%
filter(con == 1) %>%
select(aot_zscored) %>%
hist()
df4s %>%
ggplot(aes(x = conservatism, y = aot_zscored)) +
geom_bar(fun = "mean", stat = "summary")
df4s %>%
ggplot(aes(x = conservatism, y = sc_scoredz)) +
geom_bar(fun = "mean", stat = "summary")
df4s %>%
ggplot(aes(x = conservatism)) +
geom_bar()
df4s %>%
ggplot(aes(x = sc_scoredz)) +
geom_bar()
df4s %>%
ggplot(aes(x = aot_zscored)) +
geom_bar()
df4s %>%
ggplot(aes(x = nfc_zscored)) +
geom_bar()
df4s %>%
ggplot(aes(x = vl_zscored)) +
geom_bar()
facet plots
# aot
descplot_aot <- df4s %>%
# select(conservatism, conservatism_f, aot_zscored, nfc_zscored, rav_scoredz,
# sc_scoredz, vl_zscored, arctic20_ch, temp20_ch, arctic10_ch,
# temp10_ch) %>%
mutate(conservatism = as.factor(conservatism),
aot_zscored = as.numeric(aot_zscored)) %>%
select(aot_zscored, conservatism)%>%
group_by(conservatism) %>%
dplyr::summarise(n = n(),
Mean = mean(aot_zscored, na.rm=T),
sd = sd(aot_zscored, na.rm=T),
se = sd/(sqrt(n)),
var = "5. Actively Open-Minded Thinking",
min = min(aot_zscored),
max = max(aot_zscored))
descplot_aot %>%
ggplot(aes(x = conservatism, y = Mean)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin=Mean-se, ymax=Mean+se))
# sc
descplot_sc <- df4s %>%
# select(conservatism, conservatism_f, aot_zscored, nfc_zscored, rav_scoredz,
# sc_scoredz, vl_zscored, arctic20_ch, temp20_ch, arctic10_ch,
# temp10_ch) %>%
mutate(conservatism = as.factor(conservatism),
sc_scoredz = as.numeric(sc_scoredz)) %>%
select(sc_scoredz, conservatism)%>%
group_by(conservatism) %>%
dplyr::summarise(n = n(),
Mean = mean(sc_scoredz, na.rm=T),
sd = sd(sc_scoredz, na.rm=T),
se = sd/(sqrt(n)),
var = "1. Science Curiosity",
min = min(sc_scoredz),
max = max(sc_scoredz))
# nfc
descplot_nfc <- df4s %>%
# select(conservatism, conservatism_f, aot_zscored, nfc_zscored, rav_scoredz,
# sc_scoredz, vl_zscored, arctic20_ch, temp20_ch, arctic10_ch,
# temp10_ch) %>%
mutate(conservatism = as.factor(conservatism),
nfc_zscored = as.numeric(nfc_zscored)) %>%
select(nfc_zscored, conservatism)%>%
group_by(conservatism) %>%
dplyr::summarise(n = n(),
Mean = mean(nfc_zscored, na.rm=T),
sd = sd(nfc_zscored, na.rm=T),
se = sd/(sqrt(n)),
var = "4. Need For Cognition",
min = min(nfc_zscored),
max = max(nfc_zscored))
descplt_facet <- rbind(descplot_aot, descplot_sc, descplot_nfc)descplt_facet %>%
ggplot(aes(x = conservatism, y = Mean)) +
geom_bar(stat = "identity", fill = "darkseagreen") +
geom_errorbar(aes(ymin=Mean-se, ymax=Mean+se), width = .2) +
facet_wrap(~var) +
labs(x = "Conservatism", y = "Mean (z-scored)") +
theme_bw()
descplot_rav <- df4s %>%
# select(conservatism, conservatism_f, aot_zscored, nfc_zscored, rav_scoredz,
# sc_scoredz, vl_zscored, arctic20_ch, temp20_ch, arctic10_ch,
# temp10_ch) %>%
mutate(conservatism = as.factor(conservatism),
rav_scoredz = as.numeric(rav_scoredz)) %>%
select(rav_scoredz, conservatism)%>%
group_by(conservatism) %>%
dplyr::summarise(n = n(),
Mean = mean(rav_scoredz, na.rm=T),
sd = sd(rav_scoredz, na.rm=T),
se = sd/(sqrt(n)),
var = "2. Raven's Progressive Matrices",
min = min(rav_scoredz),
max = max(rav_scoredz))
descplot_vl <- df4s %>%
# select(conservatism, conservatism_f, aot_zscored, nfc_zscored, rav_scoredz,
# sc_scoredz, vl_zscored, arctic20_ch, temp20_ch, arctic10_ch,
# temp10_ch) %>%
mutate(conservatism = as.factor(conservatism),
vl_zscored = as.numeric(vl_zscored)) %>%
select(vl_zscored, conservatism)%>%
group_by(conservatism) %>%
dplyr::summarise(n = n(),
Mean = mean(vl_zscored, na.rm=T),
sd = sd(vl_zscored, na.rm=T),
se = sd/(sqrt(n)),
var = "3. Verbal Logic",
min = min(vl_zscored),
max = max(vl_zscored))
descplotca <- rbind(descplot_rav, descplot_sc, descplot_vl)descplotca %>%
ggplot(aes(x = conservatism, y = Mean)) +
geom_bar(stat = "identity", fill = "darkseagreen") +
geom_errorbar(aes(ymin=Mean-se, ymax=Mean+se), width = .2) +
facet_wrap(~var) +
labs(x = "Conservatism", y = "Mean (z-scored)") +
theme_bw()
descplotall <- rbind(descplotca, descplot_aot, descplot_nfc)descplotall %>%
ggplot(aes(x = conservatism, y = Mean)) +
geom_bar(stat = "identity", fill = "darkseagreen") +
geom_errorbar(aes(ymin=Mean-se, ymax=Mean+se), width = .2) +
facet_wrap(~var, ncol=3) +
labs(x = "Conservatism", y = "Mean (z-scored)") +
theme_bw()
desc5 <- descplotall %>%
ggplot(aes(x = conservatism, y = Mean)) +
geom_bar(stat = "identity", fill = "darkseagreen") +
geom_errorbar(aes(ymin=Mean-se, ymax=Mean+se), width = .2) +
facet_wrap(~var, ncol=3) +
labs(x = "Conservatism", y = "Mean (z-scored)") +
theme_bw()plot by sci cur
sep plots
# df4s %>%
# select(contains("20_ch"), con, rav_scoredz, sc_scoredz,
# aot_zscored, nfc_zscored, vl_zscored) %>%
# cor() %>%
# kable()Difference score btwn MR and “filler”
ch_moda <- lm(data = df4s, formula = ArcIce_2 ~ con + rav_scoredz)
ch_modb <- lm(data = df4s, formula = temp20_ch ~ con + rav_scoredz +
sc_scoredz + diatho20_ch)
summary(ch_modb)##
## Call:
## lm(formula = temp20_ch ~ con + rav_scoredz + sc_scoredz + diatho20_ch,
## data = df4s)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3610 -0.7210 -0.1114 0.4407 4.9578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.75752 0.14945 11.760 < 2e-16 ***
## con 0.27001 0.05013 5.386 1.09e-07 ***
## rav_scoredz -0.04317 0.04881 -0.884 0.377
## sc_scoredz -0.09262 0.04952 -1.870 0.062 .
## diatho20_ch 0.16558 0.03826 4.328 1.80e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 528 degrees of freedom
## Multiple R-squared: 0.1031, Adjusted R-squared: 0.09628
## F-statistic: 15.17 on 4 and 528 DF, p-value: 9.503e-12